真實性(Veracity)是巨量資料在數量(Volume)、速度(Velocity)以及多樣(Variety)三個以V開頭的特質之上，另一個以V開頭的特性描述。某種程度上，真實性是真正決定巨量資料中價值的關鍵。投資社群網站中常見對個股的未來趨勢而作買進或賣出的推薦文章，有時伴隨針對同個股而有預測結果相互矛盾的推薦文章同時發生。文獻與實務上鮮少有針這些蘊涵未來性本質的推薦性文章就其最後的真實性作核實的研究。本研究針對投資性質社群中對於台灣個股作多與作空建議的貼文進行後續短、長期的績效核實。利用小卜卦(Oraclet)塑模中：前辭、命辭、占辭與驗辭的推薦核實流程，我們先分析反應推薦貼文的立卦、驗證與查詢功能所需的關係實體圖，並建置其相對應的關聯式資料庫，之後，經由爬文技術，擷取投資社群中各個股推薦貼文的預測要素，將原先非結構化的資料彙入關聯式資料庫，完成立卦動作。然後，在立卦後的適當時機，本系統可自動地由當天的日收盤股價，進行短、長期的投資效益的計算與核實。在執行速度的提升上，本系統透過雲端化將最耗時的兩個部分，即立卦和績效計算進行分散式運算，在三個節點的模擬環境中，有效降低系統運算時間到一半。當作自動驗證推薦貼文的績效，本研究所建置的社群預測性貼文之自動驗證系統，系統性地針對包括ptt stock板與鉅享網的外資評等板兩投資社群，自從2011年以來，所有約1萬筆的個股推薦貼文逐一進行驗證。本系統是近即時性的，每一個小時即自動針上述兩社群的新個股買進或放空的推薦貼文作立卦處理，並按短、長期的時序於立卦之後進行績效驗證。除了就特定個股推薦的績效–或預測之等效真實性–作核實外，也可針對同一個股作推薦的相同或是矛盾看法的預測作彙整，以及針對社群中所有作過推薦的鄉民或是外資的歷史推薦績效作彙整以利使用者查詢。

Veracity is the last V words attributed to the nature of big data in addition to volume, velocity and variety; veracity may be the most critical factor weighing the value of data at hand. For example, there are numerous stock pick recommendation postings commonly seen in investment-oriented social webs. Bloggers or professionals alike make sell or short recommendations, most time even mingled with view-conflicting postings. However, there are few, if any, follow-ups to check their validity not only in practice, but also in research literature.This study aims to track and validate Taiwan stock recommendations from two major investment social webs of PTT-stock and cnYES-Foreign investment. In particular, we are to track short and long term yields performance of each recommendation. With reference to the Oraclet framework in verification of prediction document, this study is to extract from each recommendation three components of: preface, charge, and prognostication, which is called registration process,followed by a verification process to validate the associated performance. In doing so, ER-diagram analysis is performed and a relational database implemented accordingly to accommodate the process involved. Crawlers are designed to pull stock recommendations from the two social webs, followed by the registration and verification processes on each recommendation. Meanwhile, to speed up, the system is constructed in the framework of cloud computing, the two processes of registration and verification are run on Spark.For demonstration, the implemented Automatic Verification of Stock Recommendation (AVSR) systemkeeps registering and verifying all recommendation postings from the two investment social webs since the year of 2011; each and every of more than ten thousand recommendations have been verified.The system is almost real-time, recommendation postings are processed at every hour. Verification results and pending oraclets are aligned in time axis for easy reviews. Recommendations of similar or conflicting views are compiled in batch for comparison. Performance histories of all bloggers or professionals appearing in these webs are also tracked.

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